Monetary Policy, Product Market Competition, and Growth

Transcription

1 Monetary Policy, Product Market Competition, and Growth Philippe Aghion,EmmanuelFarhi, Enisse Kharroubi November 13, 2018 Abstract In this paper we argue that monetary easing fosters growthmoreinmorecredit-constrainedenvironments, and the more so the higher the degree of product market competition. Indeed when competition is low, large rents allow firms to stay on the market and reinvest optimally, no matter how funding conditions change with aggregate conditions. To test this prediction, we use industry-level and firm-level data from the Euro Area to look at the effects on sectoral growth and firm-level growth of the unexpected drop in long-term government bond yields following the announcement of the Outright Monetary Transactions program (OMT) by the ECB. We find that the monetary policy easing induced by OMT, contributed to raising sectoral (firm-level) growth more in more highly leveraged sectors (firms), and the more so the higher the degree of product market competition in the country (sector). Keywords: growth, financial conditions, firm leverage, competition JEL codes: E32, E43, E52. This paper was presented as the second part of Philippe Aghion s Coase Lecture at the LSE on 5 June The authors wish to thank seminar participants at the ECB Conference in Sintra, at the LSE and at the 2018 Growth and Innovation Conference at College de France, as well as the referees and editor of thyis journal, for very helpful comments. The views expressed here are those of the authors and do not necessarily represent the views of the BIS. College de France, London School of Economics, and CEPR Harvard University and NBER Bank of International Settlements 1

2 1 Introduction The President of the European Central Bank (ECB), Mario Draghi, declared at the 2014 Economic Policy Symposium in Jackson Hole that he could only do half the work by relaxing monetary policy and that Member States would have to do the other half by implementing structural reforms. In this paper we use sector and firm-level data across a set of Euro and non-euro area countries to argue that a more pro-active monetary policy is more growth-enhancing in a more competitive environment. Figure 1 below provides some motivating evidence. 1 This figure summarizes the results from a crosscountry cross sector regression where average annual sectoral growth over the period , is regressed on the interaction between liquidity dependence of the corresponding sector in the US 2 and the real shortterm interest rates countercyclicality in the country over that same period. The figure shows that moving from the lowest to the highest quartile on both, liquidity dependence and monetary policy countercyclicality, increases sectoral growth significantly in a country with below median barrier to trade and investment (BTI) whereas it has a negligible effect on growth in a country with higher than median BTI. 1 In the first part of the paper we outlay a simple analytical model of the complementarity between product market competition and monetary easing. In this model firms can make growth-enhancing investment but are subject to liquidity shocks that forces them to reinvest money in their project. Anticipating this, firms may have to sacrifice part of their investment in order to secure reinvestment in case of a liquidity shock (liquidity hoarding). A countercyclical monetary policy, which sets high interest rates in expansions and low interest rates in recessions, turns out to be growth-enhancing as it reduces the amount of liquidity entrepreneurs need to hoard to whether liquidity shocks. Moreover, the model predicts that such a countercyclical monetary policy is more growth-enhancing when competition is higher: indeed when competition is low, large rents allow firms to stay on the market and reinvest optimally, no matter how funding conditions change with aggregate conditions. 1 The appendix provides all the details of the empirical analysis underpinning computations used in Figure 1. 2 Here we follow the methodology in Rajan and Zingales (1998). 2

3 In the second part of the paper we develop our core empirical analysis. We proceed in two steps. First, we perform an industry-level analysis. Then we move on to firm-level analysis. Productivity measures including TFP- tend to be more reliable at the sector level. Moreover, sectoral analysis enables us to look at the effects of OMT and product market competition on firm demographics, in particular on new firm entry. The firm-level analysis serves as a robustness test to show that the cross-sectoral effects of OMT also hold across firms within sectors. Moreover, it allows us to use the concentration index in a firm s sector to measure the degree of product market competition faced by the firm (e.g. see Aghion et al, 2005). When performing the industry-level analysis, we consider a set of Euro-Area countries some of which were directly hit by the sovereign debt crisis (Belgium, Italy, Portugal and Spain) and others were not (Austria, France, Germany). We then use interest rate forecasts from the OECD Economic Outlook publication to compute the unexpected change in each Euro Area long-term government bond yield following the announcement of the Outright Monetary Transactions (OMT) program and we regress industry growth on: (i) the country-level unexpected change in long-term government bond yield following OMT; (ii) sectoral indebtedness; (iii) the interaction between the two; (iv) the triple interaction between the unexpected change in bond yields, sectoral indebtedness, and the country-level degree of product market competition. We show that the drop in the unexpected bond yields following OMT had a more positive effect on sectoral growth in more leveraged sectors. Moreover, this latter effect was significant only for sectors located in countries where product market regulation was low prior to OMT. Then we turn our attention to the firm-level analysis. There we put together a dataset of listed firms from eight European countries (Belgium, Denmark, France, Germany, Italy, the Netherlands, Spain and the United Kingdom). For each country in our sample we gather data on domestic banks holdings of Euro Area countries sovereign debt. Next, using daily data on the yield curve of each Euro Area country, we compute the bank-by-bank revaluation gain on the portfolio of sovereign debt holdings, stemming from the announcement of the OMT policy. And we aggregate these revaluation gains at the country-level so as to obtain a countrylevelmeasureoftheomtshock. Followingthe samemethodology as for the sector-level analysis, we regress firm-level growth on the country-level measure of the OMT shock interacted first with a firm-level measure 3

4 of indebtedness, 3 and second with competition in the firm s sector, where competition is inversely measured by the sectoral Herfindhal index. In this regression, we include the full set of country/sector fixed effects so that sectoral characteristics such as differences in competition or differences in demand are controlled for. Overall, the results of the firm-level analysis parallel to those of the sector-level analysis. First, we find a positive and significant effect of the interaction between the revaluation gain stemming from the OMT policy and firm-level indebtedness on the growth in firm-level sales and firm-level employment. Second, we find that this positive growth gain of OMT in more indebted firms, accrues particularly to firms located in more competitive sectors, i.e. in sectors where the Herfindhal index is low. The paper relates to several strands of literature. First, to the literature on macroeconomic volatility and growth. A benchmark paper in this literature is Ramey and Ramey (1995) who find a negative correlation in cross-country regressions between volatility and long-run growth. A first model to generate the prediction that the correlation between long-run growth and volatility should be negative, is Acemoglu and Zilibotti (1997) who point to low financial development as a factor that could both, reduce long-run growth and increase the volatility of the economy.. Subsequently, Aghion et al (2010) looked at the relationship between credit constraints, volatility, and the composition of investment between long-term growth-enhancing (R&D) investment and short term (capital) investment, and showed that more macroeconomic volatility is associated with a lower fraction of investment devoted to R&D and to lower productivity growth. More closely related to this paper is Aghion, Hemous and Kharroubi (2012) which showed that more countercyclical fiscal policies affect growth more significantly in sectors whose US counterparts are more credit constrained. Our paper contributes to this overall literature by introducing monetary policy and competition (or product market regulation) into the analysis. 4 Our paper also speaks to the debate on policy versus institutions as determinants of volatility and growth. Acemoglu et al (2003) and Easterly (2005) hold that both, high volatility and low long-run growth do not directly arise from policy decisions but rather from bad institutions. Our paper contributes to this debate 3 In addition to this results, the empirical analysis also shows that high debt tends to be a drag on growth but that product market regulation tends to dampen this negative effect. 4 See also Aghion and Kharroubi (2013) who look at the relationship between monetary policy and financial regulation. It shows that tighter financial regulation in the form of higher bank capital ratios- may contribute to reducing the growthenhancing effect of a more counter-cyclical monetary policy. 4

5 by showing that monetary policy matters even among industries and firms which are all located in countries with similar property rights and political institutions; yet product market competition also matters. 5 Third, we contribute to the literature on monetary policy design. In our model, monetary policy operates through a version of the credit channel (see Bernanke and Gertler 1995 for a review of the credit channel literature). 6 More specifically, our model builds on the macroeconomic literature on liquidity (e.g. Woodford 1990 and Holmström and Tirole 1998). This literature has emphasized the role of governments in providing possibly contingent stores of value that cannot be created by the private sector. Like in Holmström and Tirole (1998), liquidity provision in our paper is modeled as a redistribution from consumers to firms in the bad state of nature; however, here redistribution happens ex post rather than ex ante. This perspective is shared with Farhi and Tirole (2012), however their focus is on time inconsistency and ex ante regulation; also in their model, unlike in ours, there is no liquidity premium and therefore, under full government commitment, there is no role for a countercyclical interest rate policy. The remaining part of the paper is organized as follows. Section 2 develops a simple model to analyze the interplay between monetary policy, competition, and growth. Section 3 looks at the effect on long-term industry growth on the unexpected drop in long-term government bond yields following OMT, and at how the magnitude of this effect is itself affected by product market competition. Section 4 focuses on the firm-level analysis. And Section 5 concludes. 2 Model 2.1 Basic setup The model is a straightforward extension of that in Aghion et al (2013). The economy is populated by non-overlapping generations of two-period lived entrepreneurs. Entrepreneurs born at time have utility function = E[ +2 ],where +2 is their end-of-life consumption. They are protected by limited liability 5 See also Aghion et al (2009) who analyze the relationship between long-run growth and the choice of exchange-rate regime; and Aghion, Hemous and Kharroubi (2012) who show that more countercyclical fiscal policies affect growth more significantly in sectors whose US counterparts are more credit constrained. 6 There are two versions of the credit channel : the "balance sheet channel" and the "bank lending channel". Our model features the balance sheet channel, focusing more on the effect of interest rates on firms borrowing capacity. 5

6 and is their endowment at birth at date. Their technology set exhibits constant returns to scale. Upon being born at date, the new generation of entrepreneurs choose their investment scale 0. At the interim date +1 uncertainty is realized: it consists of both, of an aggregate shock which is either good (G) or bad (B), and of an idiosyncratic liquidity shock. The two events are independent and we denote by the probability of a good aggregate shock, and by the probability of a firm experiencing a liquidity shock. At date +1,aninterimcashflow ( ) accrues to the entrepreneur where ( ) { ( ) ( )} with ( ) ( ) and is a parameter which measures the degree of product market competition and 0 ( ) 0. Weassumeinwhatfollowsthat { } so that = (resp. = ) reflects high competition (resp. low competition) on the product market. The interim cash flow is not pledgeable to outside investors. But other returns generated by the firm are pledgeable. We assume that in the absence of a liquidity shock, the other returns are obtained already at date +1: namely, the entrepreneur generates the additional return 1,ofwhich is pledgeable to investors. 7 If the firm experiences a liquidity shock, then the additional return is earned at date +2 provided additional funds +1 are reinjected into the project in the interim period. The entrepreneur then gets 1 +1 at date +2,ofwhichonly +1 is pledgeable to investors. Entrepreneurs in the economy differ with respect to the probability of a liquidity shock. Namely: { } with. We interpret the probability as a measure of liquidity-constraint. The one period gross rate of interest at the investment date is denoted by, whereas denotes the one period gross rate of interest at the reinvestment date +1when the aggregate shock is, { ; }. We assume: Assumption 1: min { } Assumption 1 ensures that entrepreneurs are constrained and must invest at a finite scale. The next 7 The model assumes that competition only affects short-term profits and not long-run profits. It can actually be argued that if long-run profits are those associated to innovation, they would be less sensitive to competition as innovation is precisely a way to escape it. By contrast, short-term profits are those derived from existing activities and products and thereby more subject to competitive pressures. 6

7 assumption determines how easy/difficult reinvestment is, for entrepreneurs facing a liquidity shock. Assumption 2: ( ) 1 and 1 ( ) 0 1 ( ). Assumption 2 guarantees that, irrespective of the degree of product market competition, cashflows in the good state are enough to cover liquidity needs and reinvest at full scale if a liquidity shock hits. However, in the bad state, cash flows alone are enough to cover liquidity needs only if competition is low, i.e. =. If competition is high, i.e. =, and the bad state realizes, then a firm facing a liquidity shock will have to use additional liquidity set aside at the investment date if it wants to reinvest at full scale. We assume that liquidity hoarding is costly: to purchase an asset that pays-off 0 at date +1,the entrepreneur needs to hoard the amount (1 ) 0 at date, where 1. The difference ( 1) reflects the cost of liquidity hoarding. Entrepreneurs face the following trade-off: on the one hand, maximizing the amount invested in its project requires minimizing the amount of liquidity hoarded, which in turn may prevent the firm from reinvesting at large scale if it faces a liquidity shock and the economy experiences a bad aggregate shock; on the other hand, maximizing liquidity to mitigate maturity mismatch requires sacrificing initial investment scale. 2.2 Investment, liquidity hoarding and reinvestment in equilibrium Let us first consider a firm s reinvestment decision at the interim period +1. If it faces both a liquidity shock and a bad aggregate shock, a firm born at date can use its short-term profits ( ), plus the amount of hoarded liquidity 0 if any, plus the proceeds from new borrowing at date +1(the entrepreneur can borrow against the pledgeable final income +1 ) for reinvestment at date +1. More formally, if +1 [0 ] denotes the firm s reinvestment at date +1 we must have: +1 ( 0 + ( )) + +1 (1) or: ½ ¾ 0 + ( ) +1 min, 1 (2) 1 7

8 In particular, a lower interest rate in the bad state facilitates refinancing because this increases the ability to issue claims at the reinvestment date and hence reduces the need to hoard liquidity at the investment date which in turn saves on the cost of liquidity given the positive liquidity premium ( 1). Moving back to date, we can determine the equilibrium hoarding and investment at that date. Starting with initial wealth, the entrepreneur needs to raise at date from outside investors to invest in its project. In addition, the firm must anticipate the need for reinvestment if a liquidity shock hits in the bad aggregate state: to face such possibility, the entrepreneur will rely on both, liquidity hoarding to get the additional liquidities 0 at date +1and additional future borrowing by issuing new claims 1 to investors against the final pledgeable cash flow. If the return 1 to long-term projects is sufficiently large, then in equilibrium the entrepreneur chooses the maximum possible investment size, which is the investment such that all these calls on investors will have to be exactly matched by the total present expected flow of pledgeable income generated by the firm. Hence the equilibrium investment size will satisfy: 1 ( )+ (1 ) + 0 =(1 ) + +(1 ) ( ( ) ) (3) where 0 and 1 are optimally chosen in dates and +1respectively. In fact to achieve the maximum investment size the entrepreneur will borrow up to the constraint and choose the minimum amount of liquidity compatible with full reinvestment: 1 = and 0 =1 ( ) whenever the latter expression holding if is positive; otherwise liquidity hoarding can be avoided and 0 =0. Overall, if 1 is sufficiently large, the equilibrium investment size is given by: = ³ (1 ) (4) 8

9 where =[1 (1 ) ] Growth and counter-cyclical interest rates. We assume that the growth rate of total factor productivity for a firm between period and period +2is given by: +2 = (5) where is a positive scalar. Then, using the above expression (4) for entrepreneurs ex ante long-term investment,growthinthiseconomy +2 writes as : +2 =ln +2 ln =ln +ln ³ (6) (1 ) where =[1 (1 ) ] +. To derive the comparative statics of growth with respect to the cyclicality of interest rates, we consider the effect of changing the spread between the interest rates { ; } keeping the average one period interest rate at the interim date, (1 ) + = constant. A higher will then correspond to more counter-cyclical interest rates. We can rewrite the above equation as: ln +2 " =ln ln µ (1 ) 1 ( ) +# (1 ) (7) As is clear holding the average interest rate constant, growth depends on three key parameters: First the degree of interest rate countercyclicality captured here by the level of the interest rate.second,the probability for firms to face the liquidity shock and third the degree of product market competition. Let us detail below the different comparative statics. 9

10 2.4 Competition, countercyclical interest rates and growth Given Assumption 2 which states that firms need to hoard liquidity only when competition is high, we immediately get that growth when competition is low writes as ln +2 µ ( ) =ln ln 1 + while the expression for growth turns out to be ln +2 µ ( ) =ln ln 1 + (1 ) + (1 ) 1 ( ) when competition is high. 8 It follows that an increase in the countercyclicality of monetary policy, i.e. a higher interest rate, is more likely to enhance enhance growth when competition on the product market is high (i.e. when = ) thanwhenitislow: = = Moreover a countercyclical monetary policy, i.e. a higher interest rate,ismorelikelytobenefit tofirms facing a larger probability of the liquidity shock, when competition on the product market is high than when it is low: = = 8 Note that this model, with its current framework, would predict that growth is higher with lower competition. A simple extension that would make the model more realistic from this point of view would be to to introduce an escape competition effect as in Aghion et al (2005). For example by assuming that firms make a pre-innovation profit when they do not invest, and that this pre-innovation profit decreases more with competition than the post investment profit. Importantly, this would not affect the main predictions that (i) more countercyclical interest rates are more growth enhancing for firms that are more prone to liquidity shocks and (ii) that this property holds particularly when competition is high. 10

11 3 Sectoral analysis Here we look at the effect on sectoral growth of the change in unexpected bond yields following the OMT. Specifically, we consider six Euro Area countries -which commonly faced the OMT shock- but had significantly different outcomes, especially in terms of changes in government bond yields. We exploit these cross-country differences along with cross-sectoral differences in indebtedness to infer whether sectors with fragile balance sheets did actually benefit more from the fall in government bond yields for the country they operate in. In addition to this, we use differences in product market regulation among these six Euro Area countries to test how competition changes the growth effects of the accommodation episode that followed the announcement of OMT. 3.1 The economic context The European sovereign debt crisis started by the end of 2009 as several governments of Euro Area countries (most notably Greece, Portugal, Ireland, Spain and Cyprus) were facing increasing difficulties to repay or refinance their sovereign debt or to bail out over-indebted banks. These growing financial difficulties triggered calls for assistance from third parties like other Euro Area countries, the ECB and the IMF, especially as re-denomination risks mounted, i.e. the risk that these countries may have no other options than to default and exit from the Eurozone. Several initiatives were undertaken to confront this debt crisis, among which the implementation of the European Financial Stability Facility (EFSF) and European Stability Mechanism (ESM), which acted as vehicles for financial support in exchange of measures designed to address the longer-term issues of government and banking sectors financing needs. The ECB contribution to addressing the European sovereign debt crisis took several forms, including lowering policy rates and providing cheap loans of more than one trillion euro. Yet, the most decisive policy action was on 6 September 2012, by which the ECB announced free unlimited support for all Euro Area countries involved in a sovereign state bailout/precautionary programme from EFSF/ESM, through some yield lowering Outright Monetary Transactions (OMT). Arguing that divergence in short-term bond yields is an obstacle to ensuring that monetary policy is transmitted equally to 11

12 all the Eurozone s member economies, the ECB portrayed (purchases under) the OMT programme as an effective back stop to remove tail risks from the euro area and safeguard an appropriate monetary policy transmission and the singleness of the monetary policy. 9 Several studies have confirmed that following the announcement of OMT, a number of yields on Euro Area government bonds shrank considerably. For example, Altavilla et al. (2014) estimate that the Italian and Spanish 2-year government bond yields decreased by about 200 bps after the OMT announcement, yet leaving bond yields of the same maturity in Germany and France unchanged. De Grauwe and Ji (2014) suggest that the shift in market sentiment triggered by the OMT announcement accounts for most of the decline in bond yields that was observed at that time, rejecting the view that improved fundamentals have played a significant role. These results are actually consistent with the fact that OMT was never practically used. 3.2 The empirical methodology Our goal consists in finding out what real effects had the drop in government bonds yields of Euro Area countries that followed the OMT programme. To do so, we use OECD Economic Outlook quarterly projections for short and long term interest rates to infer the surprise component in the evolution of these interest rates. 10 More specifically we denote the yield on the 10-year government bond in country in quarter of year and 1 the projected yield on the 10-year government bond in country in quarter of year, conditional on all information available by the end of year We then compute the forecast error on this yield as = 1 9 Executive Board member, Benoît Cœuré, described OMT as follows: "OMTs are an insurance device against redenomination risk, in the sense of reducing the probability attached to worst-case scenarios. As for any insurance mechanism, OMTs face atrade-off between insurance and incentives, but their specific designwaseffective in aligning ex-ante incentives with ex-post efficiency." 10 Given that OMT was targeted to shorter maturity bonds (1-3 years), it would be more natural to look at those shorter maturity bonds than the 10-year bonds. In practise however, OMT affected the whole yield curve of Euro Area countries. Hence looking at the 10-year bond is still acceptable. 11 Using this methodology implies that the forecast horizon ranges from one to four quarters at most. 12

13 Here a positive forecast error reflects a higher than expected rate or yield, implying that funding conditions have unexpectedly tightened. On the contrary negative forecast errors reflect easier than expected funding conditions. Computing these forecast errors for the four most significant Euro Area countries (France, Germany, Italy and Spain) shows a number of striking patterns. First there is a sharp drop in the forecast errors on 10 year government bond yields in Spain and Italy after 2012q3. While yields were significantly larger than expected over 2011, when the sovereign debt crisis was at its height, they ended up being significantly lower than expected over 2013 and Second, interestingly, these changes do not extend to France and Germany, where the period does not provide evidence of yields significantly higher than expected as these countries were on the contrary benefiting from their safe haven status. 2 3 It is an open question to figure out how much of these changes relate to the specific OMT announcement and we do not intend to argue that OMT accounts for all these forecast errors. Yet, irrespective of the extent to which such forecast errors may be accounted for by OMT, they actually provide a good measure of the unexpected change in funding conditions in the relevant countries, and as such, they are likely to have significant real effects. In addition, these forecast errors prove to be highly correlated with actual changes in funding conditions. Whether the latter are measured using changes in interest rates or interest rate spreads on corporate lending, the data shows a tight link between these changes and the unexpected drop in governement bond yields Empirical specification To investigate the real effects of the unexpected drop in government bonds yields that followed the announcement of OMT, we consider a difference-in-difference approach focusing on the two periods of and For each of these periods, we compute the average forecast error on 10-year government bond yields and take the difference as a measure of the unexpected easing in funding conditions. 13

14 We then build an empirical specification linking this country-wide measure of lower funding costs to growth at the industry level. Specifically we take as a dependent variable the growth rate at the sector level for each industry-country pair of the sample under study over Given data availability, we can look at growth in four different variables: real value added, real labour productivity (real value added per worker), real capital productivity (real value added to real capital stock) and total factor productivity. On the right hand side, in addition to saturating the specification with industry and country fixed effects, we control for growth at the industry level over the period , so that all results can be interpreted as changes in growth relative to the reference period. Our main variable of interest is the interaction between: (i) an industry s balance sheet indicator -denoted (debt); (ii) and the unexpected change in a country s funding conditions -denoted (omt). As explained above, the latter variable is computed as the difference between long term government bond yield average forecast error over , denoted and denoted : (omt) = Turning to industry balance sheet indicators, we consider two measure of indebtedness. A narrow indicator is the stock of bank debt as a ratio of total equity. A wider indicator is the stock bank debt and bonds as ratio of total equity. In addition we will also make use of liquidity indicators by looking at the ratio of current bank debt to equity or current bank debt and bonds to equity, current liabilities being those with a maturity less than one year. Importantly, industry balance sheet indicators are measured prior to the period, namely either in 2010 or in Denoting ( ) the growth rate of industry in country over the period (over the period ), (reg) thedegreeofproductmarket regulation in country, and industry and country fixed effects, and letting denote an error term, our baseline regression is expressed as follows: = (debt) + 11 (debt) (reg) (8) + 2 (debt) (omt) + 21 (debt) (omt) (reg) + 14

15 Here, the coefficient 11 determines how product market regulation affects the relationship between corporate indebtedness and growth while the coefficient 21 determines how product market regulation affects the differential relationship between the change in funding conditions and growth. Intuitively and consistent with the model derived above, we would expect corporate indebtedness to be a drag on growth, i.e. 10 0, while we would expect product market regulation to reduce the growth cost of corporate indebtedness, i.e 1 0. In addition, a positive coefficient 2 for instance would imply that highly indebted sectors benefit disproportionately more from an unexpected drop in funding costs while a negative coefficient 21 for instance would imply that product market regulation typically reduces the growth benefit of lower funding cost for the most indebted sectors. 3.4 Data Sources Our data sample focuses on the big four Euro Area countries France, Germany, Italy and Spain to which we add Austria, Belgium and Portugal. Focusing on this limited set of countries is driven by data availability considerations. Our data come from various sources. Industry-level real value added, employment, capital stock and total factor productivity are drawn from the European Union (EU) KLEMS data set and cover the whole economy wherever data is available. Our source for sectoral balance sheet data is the BACH database. We draw from this dataset the sector-level balance sheet data for equity, bank debt, bonds, current bank debt and current bonds and financial payments. We carry out the estimations using the balance sheet data for either year 2010 or 2012 so that in both cases, the announcement of OMT would not contaminate these measures. 12,13 The product market regulation data comes from the OECD and is measured for the year Finally, forecast errors in government bond yields are computed using quarterly data from the different vintages of the OECD Economic outlook database In addition, the data for 2010 is not affected by the sovereign debt crisis. 13 Using the actual balance sheet data instead of those pertaining to the corresponding US sector has two advantages. First, we can exploit the cross-country heterogeneity as the same sector features pretty diverse balance sheets when looking at different sectors. Second, the European sovereign debt crisis hit some countries more severely than others. This has prompted very diverse change in sectoral indebtedness across countries. These two features represents two sources of heterogeneity that can usefully be exploited in our context. 14 The OECD publishes twice a year (June and December) forecasts over a two year horizon for a number of macroeconomic variables. We consider for each year +1forecasts of the December issue of year so that the forecast horizon nevers exceeds four quarters. 15

16 3.5 Results Table 1 provides the estimation results for specification (8) under different parameter restrictions for each of the four different growth dependent variables referred to above (value added, labour productivity, capital productivity and total factor productivity). In addition Table 1 estimations use the ratio of bank debt to equity as a measure of sectoral indebtedness. Table 2 provides a similar set of regressions, but using the wider measure of sectoral indebtedness, the ratio of bank debt and bonds to total equity. In a nutshell, the empirical results suggest that the interaction of the unexpected reduction in government bonds yields following OMT and corporate indebtedness, irrespective of the specific measure considered, seem to have had a significant effect on industry growth, but only to the extent that cross-country differences in product market competition are taken into account. More precisely, looking at the second and third row of Table 1, the estimation results show that the sectoral bank debt to equity ratio on its own, has no effect on growth. However this actually hides a significant positive effect of product market regulation, which acts to dampen thenegativeeffect of indebtedness on growth. Put differently, a large bank debt to equity ratio acts as a drag on growth but only insofar as product markets are relatively unregulated. Product market regulation therefore acts to reduce the burden of high debt on growth. Interestingly, this result holds similarly for all our four growth variables, including total factor productivity growth. It also holds in a similar fashion when using the wide ratio -bank debt and bonds to equity- as a measure of sectoral indebtedness instead of the narrow ratio -bank debt to equity- (second and third row of Table 4), although it is fair to say that the latter estimation results show weaker significance. 1 2 Turning now to the fourth and fifth row of Table 1, we can see that, on its own a drop in funding costs -as captured by the change in forecast errors on government bond yields- does not benefit inasignificant way to either more or less indebted sectors, this holding equally, irrespective of the specific definition of sectoral indebtedness (see fourth and fifth row of Table 2). If anything, the interaction between the drop in the government bond yield and the sectoral bank debt to equity ratio carries a negative, although not significant, 16

17 coefficient, suggesting that highly indebted sectors would benefit lessfromeasierfinancial conditions, a result that seems at odds with any simple intuition. Yet as was the case for sectoral indebtedness, this inconclusive result hides conflicting patterns as highly indebted sectors do actually benefit more from easier funding conditions, but only in countries where the index for product market regulation is rather low. Otherwise, in countries with tightly regulated product markets, easier funding conditions either benefit equally to sectors with high and low debt, or they actually benefit more to sectors with lower indebtedness. Moreover, the turning point for the index of product market regulation beyond which the effect of the interaction term turns from positive to negative (6th row in Table 3 and Table 4) shows remarkable consistency across the different estimations, irrespective the specific growth dependent variable and irrespective of the specific definition of sectoral indebtedness. 3.6 Quantifying the effect of product market regulation. Based on the empirical results described above, we can draw conclusions for each country of our sample as to what extent sectors located in each of these countries may have benefited from the unexpected drop in long term yields that followed OMT. To do so, we consider the product market regulation index in each country and simulate two scenarios. First we look at the change in real value added growth stemming from a 10% increase in the bank debt to equity ratio. Second, we look at the change in real value added growth stemming from the combination of a 10% increase in the bank debt to equity ratio and a 100 basis points drop unexpected drop in long term government bonds yields. Two main conclusions can be drawn from this exercise. First there are two groups of countries: Austria, Germany and Italy on the one hand and Belgium, France and Spain on the other hand. In the former group, where the product market regulation index is rather low, an increase in indebtedness tends to reduce growth while the combination of an increase in indebtedness and a reduction in government bond yields tends to raise growth. Interestingly, in these computations which assume a 100 basis point unexpected reductioningovernmentbondyields,thelatter positive effect tends to dominate from a quantitative standpoint the former negative effect. In the second group of countries, Belgium, France and Spain, where product market regulation is rather tight, indebtedness 17

18 has no significant direct effect on growth. Moreover, the reduction is government bonds yields that followed OMT has rather, if anything, benefited to sectors with relatively low bank debt to equity. Tight product market regulation has therefore acted to shield the economy from the cost of high indebtedness. However at the same time, it has also redirected the benefits of lower funding costs to those sectors which had relatively stronger balance sheets, i.e. lower bank debt and hence arguably those sectors that were less in need for support Growth and the composition of investment. The empirical results in Table 1 and Table 2 show a positive growth effect of unexpectedly lower government bond yields that is larger for more indebted sectors located in countries with lower product market regulation indexes. In order to link more closely this empirical evidence with our model, we investigate whether investment and/or the composition of investment react to lower unexpected bond yields in the same way as value added and productivity. To do so, we use as a dependent variable the average investment to capital ratio over the period (columns 1 to 3 and columns 7 to 9) or alternatively the average ratio of capital expenditure to investment over the period (columns 4 to 6 and columns 10 to 12). In columns 1 to 6, we measure sectoral leverage using the debt to equity ratio while in columns 7 to 12, we consider the more comprehensive measure of debts and bonds to equity ratio. The empirical results show that the investment to capital ratio did not respond significantly to the unexpected drop in government bond yields, irrespective of the specific measure of sectoral leverage (columns 1to3andcolumns7to9). Hencethepositivegrowtheffect described above does not stem from higher investment. However looking at the composition of investment, there is evidence that capital expenditures as a share of total investment have increased more in sectors with higher leveraged located in countries where the unexpected drop in bond yields was larger. Yet, as was the case with productivity growth, this positive effect tends to be dampened in countries with tighter product market regulations (columns 6 and 12). Overall, consistent with the model, the growth effect of lower funding costs has more to do with a 18

19 change in the composition of investment (with capital expenditures having a larger weight) than with an increase in the volume of investment Investigating the role of liquid liabilities Up to now, the empirical analysis has focused on the role of leverage and indebtedness in affecting growth at the sector-level and as a transmission channel for the effects of changes in funding conditions on growth. In this section, we aim at expanding the analysis to investigate the role of liquid liabilities. Specifically we consider bank debt and bonds with a less than one year maturity and build two sector-level indicators of liquid financial liabilities: (i) the ratio between bank debt with a less than one year maturity and equity and (ii) the ratio between bank debt and bonds with a less than one year maturity and equity. We then extend the empirical specification (8) to allow the indicator of liquid financial liabilities -denoted cde- toaffect growth independently of leverage. Specifically, we first test whether holding liquid financial liabilities has a direct effectongrowthatthesectorlevel,beyondandabovethedirecteffect of leverage and indebtedness; and how product market regulation affects this direct linkage if any. = (bs cde) + 1 (bs cde) (reg) + 2 (bs) (omt) + 21 (bs) (omt) (reg) + (9) For example it may well be that holding debt with a short maturity actually amplifies the drag from leverage on growth as such sectors are forced to forego profitable growth opportunities in order to ensure they will be able to service their debt, particularly those maturing quickly. Second, we test whether holding liquid financial liabilities affects the benefits a sector can derive from changes in funding conditions that followed OMT: = (bs) + 1 (bs) (reg) + 2 (bs cde) (omt) + 21 (bs cde) (omt) (reg) + (10) 19

20 Here it might well be that sectors with significant amounts of short term debts may actually benefit more from lower funding costs, as these debts are maturing more quickly and hence provide more opportunities to benefit from the lower funding costs. The empirical evidence gathered in Table 4 shows that neither the ratio of current debt to equity nor the ratio of current debt and bonds to equity seem to have a direct effect on growth, beyond and above that of leverage. Estimation results of specification (9) suggest that what has a direct effect on growth is the amount not the maturity of financial liabilities in relation to the level of equity. Things are different when it comes to how the reduction in funding costs transmits to growth: Results from estimating specification (10) suggest that when a sector holds liquid liabilities, this raises the benefit that can be expected from a reduction in government bond yields, but also makes product market regulation more costly. This is consistent with the view that when liabilities have a shorter maturity, firms can more quickly reap the benefit ofrefinancing their debts on more favorable terms. Yet the results suggest that firms may have less incentives to turn this "financial windfall profit" into real decisions that would deliver higher growth when they are holding monopoly rents. Product market regulation therefore acts to decouple firms financial strength from firms real decisions Interest payments and firm demography So far, we have established that sectors more heavily indebted benefited disproportionately more from the drop in long term interest rates that followed the announcement of the OMT program. Also, this benefit was larger in countries where product market regulation was lower. In this section, we aim at disentangling the channels through which these two results can take place. Specifically, we focus on two possible channels. The first one relates to the financial effects of the OMT policy. More specifically, we ask the question of whether sectors did benefit a reduction on their interest payments after the OMT shock and the more so for those more heavily indebted. Table 5 below provides evidence showing that this is indeed the case: Interest payments to equity did fall by more for more heavily indebted sectors located in countries where the fall in long-term interest rates was larger. Columns (5)-(8) also show that a similar result holds for the change in 20

21 interest payments to equity. However, product market regulation acted to reduce the drop in (the change) interest payments to equity. On this last result, a couple of different interpretations are possible. On the onehand,itmaybethatfirms facing strong competition are eager to refinance existing debt to cash in the benefit of lower interest rates. It may also be that firm debt carries a shorter maturity when competition is stronger. It may finally be that banks are less willing to engage in debt renegotiation or debt refinancing whenproductmarketregulationistighter. 5 Next we turn to firm demography. Here we aim at finding out whether the previously identified real effects of OMT did take place through a change in firm demography. To do so, we focus on two variables. First the entry rate, defined as the fraction of sectoral employment in newly created firms, tends to depend positively on the interaction between sectoral indebtedness and the unexpected drop in government bond yields following OMT. In addition we observe that this positive effect was dampened in countries where product market regulation is tighter. Interestingly sectoral indebtedness has no independent significant effect on entry rates. Turning to post entry employment growth, we observe a similar set of result, product market regulation acting to limit the benefit heavily indebted sectors can draw from the drop in government bond yields. 6 Overall, these results confirm that product market regulation tends to limit entry and post-entry growth, by reducing the effect of easier funding conditions in highly indebted sectors. 4 The firm-level analysis In this section, we explore the relationship between credit constraints, performance, and the interplay between OMT and product market competition at the firm level. We start by describing the empirical specification and the data and measurement. And then we present our empirical results. 21

22 4.1 The empirical specification To study the real effects of OMT at the firm-level, we use the following baseline difference in difference specification: log = +log +log +log + (11) The dependent variable, denoted, is the average log of a firm-level real outcome like sales or employment, over the period In this notation, firms are denoted with the subscript, sectors are denoted with the subscript and countries are denoted with the subscript. On the right hand side, we include the full set of sector-country fixed effects to control for any sector-specific shock. All the estimated effects are therefore measured relative to their sector-country average. In addition, we control for the firm-level real outcome in the period that preceded the implementation of OMT. is a firm-level variable (in particular the firm s leverage), and the term log captures the interaction between this firm-level variable and OMT. Thus, we test whether more leveraged firms benefit more or less from the OMT policy. Next, we introduce competition at the sector level, proxied by concentration indexes. We thus estimate the equation: log = +log +log +log +log +log + (12) The interaction term log captures how the effect of leverage varies between high vs. low concentration sectors; and the triple interaction log captures the extent to which the effect of OMT on more leveraged firms, was itself stronger for firms located in high versus low concentration sectors. 4.2 The data We now present the data and how we construct our firm-level and concentration measures. 22

23 4.2.1 Firm-level data We use firm-level data on income statement and balance sheet form Worldscope. Our data covers Belgium, Denmark, France, Germany, Italy, thenetherlands, Spain, Portugal and the United Kingdom. From this data source, we retrieve data on sales, employment, market capitalization, staff costs and total liabilities, which will be our different dependent variable. From the same data source, we also retrieve data on firm leverage, which we measure either as the (log of) the ratio of total assets to total equity or the (log of) the ratio of total assets to total liabilities. Overall, we have between and firms depending on the specification we use Building our monetary policy shock variable. For each country in our sample we consider the portfolio of sovereign debt holdings held by national banks. This data comes from the EBA 2012 capital exercise. This allows us to compute, for each country, the average portfolio of sovereign debt holdings, with a breakdown by government issuer and maturity. We then compute the revaluation gain on this portfolio due to the OMT policy by taking for each maturity and sovereign issuer the corresponding change in bond yields around the time of announcement of the OMT policy, using daily data on the sovereign yield curve. Next, to transform changes in yields into changes in price, we assume that all sovereign bond holdings are zero-coupon bonds. Summing up across all available maturities and sovereign issuers we end up with the average revaluation gain each country s banking sector benefited from as a result of the OMT policy. Our identification assumption is therefore that a firm with a given leverage from a given sector should grow faster when located in a country where the banking sector experienced a larger revaluation gain as a result of the OMT policy. Formally, let us denote by ; the amount of bank s holdings of sovereign debt of country of maturity and (resp. )theyieldoncountry government debt of maturity before (resp. after) the OMT shock. We compute the revaluation gain ; as " 1 ; = ; # 23